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Many subcellular compartments are biomolecular condensates made of multiple components, often including several distinct proteins and nucleic acids. However, current tools to measure condensate composition are limited and cannot capture this complexity quantitatively because they either require fluorescent labels, which can perturb composition, or can distinguish only one or two components. Here we describe a label-free method based on quantitative phase imaging and analysis of tie-lines and refractive index to measure the composition of reconstituted condensates with multiple components. We first validate the method empirically in binary mixtures, revealing sequence-encoded density variation and complex ageing dynamics for condensates composed of full-length proteins. We then use analysis of tie-lines and refractive index to simultaneously resolve the concentrations of five macromolecular solutes in multicomponent condensates containing RNA and constructs of multiple RNA-binding proteins. Our measurements reveal an unexpected decoupling of density and composition, highlighting the need to determine molecular stoichiometry in multicomponent condensates. We foresee this approach enabling the study of compositional regulation of condensate properties and function.
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http://dx.doi.org/10.1038/s41557-025-01928-3 | DOI Listing |
Anal Chem
September 2025
State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR 999077, China.
Mass spectrometry imaging (MSI) is a label-free technique that enables the visualization of the spatial distribution of thousands of ions within biosamples. Data denoising is the computational strategy aimed at enhancing the MSI data quality, providing an effective alternative to experimental methods. However, due to the complex noise pattern inherent in MSI data and the difficulty in obtaining ground truth from noise-free data, achieving reliable denoised images remains challenging.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
September 2025
Molecular Imaging Program at Stanford, Department of Radiology, School of Medicine, Stanford University, Palo Alto, CA 94304.
The biophysical properties of single cells are crucial for understanding cellular function and behavior in biology and medicine. However, precise manipulation of cells in 3-D microfluidic environments remains challenging, particularly for heterogeneous populations. Here, we present "Electro-LEV," a unique platform integrating electromagnetic and magnetic levitation principles for dynamic 3-D control of cell position during separation.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Information Technology, Uppsala University, Uppsala, Sweden.
For effective treatment of bacterial infections, it is essential to identify the species causing the infection as early as possible. Current methods typically require hours of overnight culturing of a bacterial sample and a larger quantity of cells to function effectively. This study uses one-hour phase-contrast time-lapses of single-cell bacterial growth collected from microfluidic chip traps, also known as a "mother machine".
View Article and Find Full Text PDFPLoS One
September 2025
School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
Background: When analyzing cells in culture, assessing cell morphology (shape), confluency (density), and growth patterns are necessary for understanding cell health. These parameters are generally obtained by a skilled biologist inspecting light microscope images, but this can become very laborious for high-throughput applications. One way to speed up this process is by automating cell segmentation.
View Article and Find Full Text PDFJ Vis Exp
August 2025
Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology.
We present multimodal confocal Raman micro-spectroscopy (RS) and tomographic phase microscopy (TPM) for quick morpho-chemical phenotyping of human breast cancer cells (MDA-MB-231). Leveraging the non-perturbative nature of these advanced microscopy techniques, we captured detailed morpho-molecular data from living, label-free cells in their native physiological environment. Human bias-free data processing pipelines were developed to analyze hyperspectral Raman images (spanning Raman modes from 600 cm to 1800 cm, which uniquely characterize a wide range of molecular bonds and subcellular structures), as well as morphological data from three-dimensional refractive index tomograms (providing measurements of cell volume, surface area, footprint, and sphericity at nanometer resolution, alongside dry mass and density).
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